One of the cornerstones of sustainable urban growth and overall well-being is universal access to clean drinking water. However, dangerous chemical toxins that come from industrial pollutants, agricultural runoff, and insufficient waste disposal systems pose a greater threat to urban water sources. This study examines how large linguistic models (LLMs) might revolutionize the estimate and mitigation of problems related to water pollution. LLMs provide important tidbits of information about the origins, distribution, and health impacts of toxins such as heavy metals, nitrates, and natural poisons by combining enormous datasets, incorporating logical writing, systems of administration, and continuous natural observation. Their ability to coordinate several information sources supports predictive display, enabling early detection of pollution risks, and facilitating the implementation of quick response actions. Contextual analyses demonstrate the practical application of LLMs in urban environments, where they support the planning of moderation techniques, the assessment of water quality boundaries, and the measurement of toxic levels. These artificial intelligence-powered devices provide significant advantages in treating waterborne infections linked to substance exposure, such as gastrointestinal disorders and long-term chronic health effects. Notwithstanding their exceptional potential, LLMs must be received by addressing issues related to information ethics, the executives’ value in water assets, and the need for simple computations. This multidisciplinary strategy offers a path toward hard water systems and is in line of the global manageability goals. In addition to filling extension gaps between networks, analysts, and policymakers, LLMs promote specialized capacity. Urban communities can improve everyone’s drinking water’s sustainability, openness, and well-being by incorporating these high-level concepts into metropolitan water management.